Descriptions

Our goal is to detect boundaries of objects or surfaces
occurring in an arbitrary image. We present a new approach
that discovers boundaries by sequential labeling of
a given set of image edges. A visited edge is labeled as
on or off a boundary, based on the edge’s photometric and
geometric properties, and evidence of its perceptual grouping
with already identified boundaries. We use both local
Gestalt cues (e.g., proximity and good continuation), and
the global Helmholtz principle of non-accidental grouping.
A new formulation of the Helmholtz principle is specified
as the entropy of a layout of image edges. For boundary
discovery, we formulate a new, policy iteration algorithm,
called SLEDGE. Training of SLEDGE is iterative. In each
training image, SLEDGE labels a sequence of edges, which
induces loss with respect to the ground truth. These sequences
are then used as training examples for learning
SLEDGE in the next iteration, such that the total loss is
minimized. For extracting image edges that are input to
SLEDGE, we use our new, low-level detector. It finds salient
pixel sequences that separate distinct textures within the image.
On the benchmark Berkeley Segmentation Datasets
300 and 500, our approach proves robust and effective. We
outperform the state of the art both in recall and precision
for different input sets of image edges.